Global Journal of Economics and Business

Volume 13 - Issue 2 (6) | PP: 176 - 185 Language : English
DOI : https://doi.org/10.31559/GJEB2023.13.2.6
487
37

Using the Discriminant Analysis to Classify the Income of Households in Sinnar State, Sudan (2021)

Abdalrahim Ahmed Gissmalla ,
Adel Ali Ahmed
Received Date Revised Date Accepted Date Publication Date
27/2/2023 12/3/2023 4/4/2023 8/5/2023
Abstract
The purpose of this study was to distinguish between sufficient and insufficient income and to identify the most discriminating factors that influence income. The data was obtained from households in Sinnar through a structured questionnaire addressed to the heads of families, a sample of (800) households (417) had sufficient incomes, and (383) had insufficient incomes. Discriminate analysis and decision trees were applied with the help of the (SPSS) program. The results suggested that the discrimination model applied had a good fit with the data obtained from the sample and that 7 of the 24 variables used in discrimination were statistically significant. The most important discriminating variables were the evaluation of the standard of living and borrowing to cover the family's living expenses. The research showed that the possible error in discriminate function model specificity does not exceed 14.2% compared to decision trees where the possible error does not exceed 14.5%. The research study recommended the use of a statistical discrimination model to discriminate between a sufficient income and insufficient income and the use of decision trees to classify the administrative unit of Sinnar according to income.


How To Cite This Article
Gissmalla , A. A. & Ahmed , A. A. (2023). Using the Discriminant Analysis to Classify the Income of Households in Sinnar State, Sudan (2021). Global Journal of Economics and Business, 13 (2), 176-185, 10.31559/GJEB2023.13.2.6

Copyright © 2024, This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.